Variable selection in additive quantile regression using nonconcave penalty |
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Authors: | Kaifeng Zhao |
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Affiliation: | Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore |
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Abstract: | This paper considers variable selection in additive quantile regression based on group smoothly clipped absolute deviation (gSCAD) penalty. Although shrinkage variable selection in additive models with least-squares loss has been well studied, quantile regression is sufficiently different from mean regression to deserve a separate treatment. It is shown that the gSCAD estimator can correctly identify the significant components and at the same time maintain the usual convergence rates in estimation. Simulation studies are used to illustrate our method. |
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Keywords: | Additive models oracle property SCAD penalty schwartz-type information criterion |
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